{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "def analyze_single_sample(sample):\n",
    "    # Display instruction evolution\n",
    "    def display_instruction_evolution(instruction_data):\n",
    "        html = f\"<h3>Original Instruction: {instruction_data['original_instruction']}</h3>\"\n",
    "        html += \"<table><tr><th>Stage</th><th>Input Instruction</th><th>Final Evolved Instruction</th></tr>\"\n",
    "        \n",
    "        for stage in instruction_data['stages']:\n",
    "            html += f\"<tr><td>{stage['stage']}</td><td>{stage['input_instruction']}</td><td>{stage['final_evolved_instruction']}</td></tr>\"\n",
    "            \n",
    "        html += f\"<tr><td>Final</td><td colspan='2'>{instruction_data['final_instruction']}</td></tr>\"\n",
    "        html += \"</table>\"\n",
    "        \n",
    "        display(HTML(html))\n",
    "    \n",
    "    display_instruction_evolution(sample)\n",
    "    \n",
    "    # Word count analysis\n",
    "    def word_count(text):\n",
    "        return len(text.split())\n",
    "    \n",
    "    stage_word_counts = {0: word_count(sample['original_instruction'])}\n",
    "    for i, stage in enumerate(sample['stages']):\n",
    "        stage_word_counts[i+1] = word_count(stage['final_evolved_instruction'])\n",
    "    # stage_word_counts[len(sample['stages'])] = word_count(sample['final_instruction'])\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(list(stage_word_counts.keys()), list(stage_word_counts.values()), marker='o')\n",
    "    plt.title('Word Count of Instructions by Stage')\n",
    "    plt.xlabel('Stage (0: Original, 0 to N-1: Intermediate, N: Final)')\n",
    "    plt.ylabel('Word Count')\n",
    "    plt.xticks(range(0, len(sample['stages']) + 1))\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "    # Display methods used in each stage\n",
    "    def display_methods(instruction_data):\n",
    "        html = \"<h3>Methods Used in Each Stage</h3>\"\n",
    "        html += \"<table><tr><th>Stage</th><th>Method</th></tr>\"\n",
    "        \n",
    "        for stage in instruction_data['stages']:\n",
    "            html += f\"<tr><td>{stage['stage']}</td><td>{stage['optimized_method'][:500]}...</td></tr>\"\n",
    "        \n",
    "        html += \"</table>\"\n",
    "        \n",
    "        display(HTML(html))\n",
    "    \n",
    "    display_methods(sample)\n",
    "    \n",
    "    # Display statistics\n",
    "    num_stages = len(sample['stages'])\n",
    "    print(f\"Number of stages: {num_stages}\")\n",
    "    \n",
    "    # Display evolved instructions and feedbacks\n",
    "    for i, stage in enumerate(sample['stages']):\n",
    "        print(f\"\\nStage {i + 1}\")\n",
    "        print(\"Evolved Instructions:\")\n",
    "        for j, instruction in enumerate(stage['evolved_instructions']):\n",
    "            print(f\"  {j + 1}. {instruction}\")\n",
    "        print(\"\\nFeedbacks:\")\n",
    "        for j, feedback in enumerate(stage['feedbacks']):\n",
    "            print(f\"  {j + 1}. {feedback}\")\n",
    "\n",
    "    # Display final instruction\n",
    "    print(f\"\\nFinal Instruction: {sample['final_instruction']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change your path\n",
    "\n",
    "evolved_instruction_path = 'the_tomb_evolved-3e_batch1.json'\n",
    "\n",
    "with open(evolved_instruction_path, 'r') as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Original Instruction: Please verify my approach to solving the following problem: In triangle ABC, there are 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side AC. How many different quadrilaterals can be formed by selecting four of these points? I considered two cases: (1) two collinear points on one side and two points on different sides, and (2) two points on one side and two points on another side. My calculation is as follows:\n",
       "\n",
       "$$\n",
       "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
       "$$\n",
       "\n",
       "Is this method and the resulting expression accurate?</h3><table><tr><th>Stage</th><th>Input Instruction</th><th>Final Evolved Instruction</th></tr><tr><td>1</td><td>Please verify my approach to solving the following problem: In triangle ABC, there are 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side AC. How many different quadrilaterals can be formed by selecting four of these points? I considered two cases: (1) two collinear points on one side and two points on different sides, and (2) two points on one side and two points on another side. My calculation is as follows:\n",
       "\n",
       "$$\n",
       "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
       "$$\n",
       "\n",
       "Is this method and the resulting expression accurate?</td><td>```Optimized Instruction\n",
       "Step 1:\n",
       "#Methods List# Incorporate hypothetical scenarios involving different triangle configurations; ask for evaluation of alternative calculation methods; require explanation of each step in the solution, including assumptions and reasoning; identify potential errors in the given approach.\n",
       "\n",
       "Step 2:\n",
       "#Plan# Rewrite the instruction to consider a hypothetical scenario where the triangle's sides have different numbers of points; ask for an evaluation of the given solution compared to alternative methods; require a detailed explanation of the reasoning behind each step, including any assumptions made; identify and justify any potential errors in the calculation.\n",
       "\n",
       "Step 3:\n",
       "#Rewritten Instruction Process# In a hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution against alternative methods, explain the rationale behind each step, and identify any potential errors.\n",
       "\n",
       "Step 4:\n",
       "#Review Process# Ensure the rewritten instruction includes the hypothetical scenario, evaluation of alternative methods, detailed explanation of reasoning, and identification of potential errors. Adjust as necessary to maintain clarity and answerability while increasing complexity.\n",
       "\n",
       "Step 5:\n",
       "#Finally Rewritten Instruction Process# In a hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution against alternative methods, explain the rationale behind each step, and identify any potential errors in the calculation. Is the given method and resulting expression accurate, considering the hypothetical scenario and alternative approaches?\n",
       "```</td></tr><tr><td>2</td><td>```Optimized Instruction\n",
       "Step 1:\n",
       "#Methods List# Incorporate hypothetical scenarios involving different triangle configurations; ask for evaluation of alternative calculation methods; require explanation of each step in the solution, including assumptions and reasoning; identify potential errors in the given approach.\n",
       "\n",
       "Step 2:\n",
       "#Plan# Rewrite the instruction to consider a hypothetical scenario where the triangle's sides have different numbers of points; ask for an evaluation of the given solution compared to alternative methods; require a detailed explanation of the reasoning behind each step, including any assumptions made; identify and justify any potential errors in the calculation.\n",
       "\n",
       "Step 3:\n",
       "#Rewritten Instruction Process# In a hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution against alternative methods, explain the rationale behind each step, and identify any potential errors.\n",
       "\n",
       "Step 4:\n",
       "#Review Process# Ensure the rewritten instruction includes the hypothetical scenario, evaluation of alternative methods, detailed explanation of reasoning, and identification of potential errors. Adjust as necessary to maintain clarity and answerability while increasing complexity.\n",
       "\n",
       "Step 5:\n",
       "#Finally Rewritten Instruction Process# In a hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution against alternative methods, explain the rationale behind each step, and identify any potential errors in the calculation. Is the given method and resulting expression accurate, considering the hypothetical scenario and alternative approaches?\n",
       "```</td><td>In a multi-level hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, with points that can move along the sides, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution and alternative methods, including their advantages and disadvantages, explain the rationale behind each step, and identify any potential errors in the calculation. Reflect on the decision-making process and predict possible challenges in the calculation, proposing solutions to overcome them. Consider the implications of moving points and the robustness of your solution against potential errors.</td></tr><tr><td>3</td><td>In a multi-level hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, with points that can move along the sides, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution and alternative methods, including their advantages and disadvantages, explain the rationale behind each step, and identify any potential errors in the calculation. Reflect on the decision-making process and predict possible challenges in the calculation, proposing solutions to overcome them. Consider the implications of moving points and the robustness of your solution against potential errors.</td><td>In a multi-level hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, with points that can move along the sides, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution and alternative methods, including their advantages and disadvantages, explain the rationale behind each step, and identify any potential errors in the calculation. Reflect on the decision-making process and predict possible challenges in the calculation, proposing solutions to overcome them. Consider the implications of moving points and the robustness of your solution against potential errors, while also contemplating the adaptability of your strategy based on previous errors in similar problems.</td></tr><tr><td>Final</td><td colspan='2'>In a multi-level hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, with points that can move along the sides, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution and alternative methods, including their advantages and disadvantages, explain the rationale behind each step, and identify any potential errors in the calculation. Reflect on the decision-making process and predict possible challenges in the calculation, proposing solutions to overcome them. Consider the implications of moving points and the robustness of your solution against potential errors, while also contemplating the adaptability of your strategy based on previous errors in similar problems.</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<h3>Methods Used in Each Stage</h3><table><tr><th>Stage</th><th>Method</th></tr><tr><td>1</td><td>\n",
       "You are an Instruction Rewriter that rewrites the given #Instruction# into a more complex version.\n",
       "Please follow the steps below to rewrite the given \"#Instruction#\" into a more complex version.\n",
       "\n",
       "Step 1: To generate a list of methods to make instructions more complex, consider incorporating elements that challenge AI's understanding by introducing hypothetical scenarios, asking for the evaluation of multiple solutions, and requiring the explanation of steps taken. Additionally, include methods ...</td></tr><tr><td>2</td><td>\n",
       "You are an Instruction Rewriter that rewrites the given #Instruction# into a more complex version.\n",
       "Please follow the steps below to rewrite the given \"#Instruction#\" into a more complex version.\n",
       "\n",
       "Step 1: To generate a list of methods to make instructions more complex, consider incorporating elements such as: \n",
       "1.1. Introducing multi-level hypothetical scenarios that require the AI to consider various possible outcomes and their implications.\n",
       "1.2. Asking for the evaluation of multiple solutions, ...</td></tr><tr><td>3</td><td>\n",
       "You are an Instruction Rewriter that rewrites the given #Instruction# into a more complex version.\n",
       "Please follow the steps below to rewrite the given \"#Instruction#\" into a more complex version.\n",
       "\n",
       "Step 1: To create a list of methods for enhancing instruction complexity, consider integrating:\n",
       "1.1. Nested hypothetical scenarios that explore the implications of various outcomes on multiple levels.\n",
       "1.2. Comparative analysis of solutions, requiring the evaluation of their relative effectiveness in di...</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stages: 3\n",
      "\n",
      "Stage 1\n",
      "Evolved Instructions:\n",
      "  1. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List# - Incorporate mathematical notations for clarity. - Request a detailed step-by-step explanation of the verification process. - Ask for confirmation on the correct usage of combinatorial formulas. - Inquire about potential alternative approaches to solving the problem.\n",
      "\n",
      "Step 2:\n",
      "#Plan# - Add mathematical notations to clarify the combinatorial expressions. - Request a detailed, sequential verification of the solution's logic and calculations. - Seek confirmation on the proper application of combinatorial formulas. - Encourage consideration of alternative problem-solving strategies.\n",
      "\n",
      "Step 3:\n",
      "#Rewritten Instruction# - Please meticulously verify my approach to solving the following problem, employing combinatorial formulas: In triangle \\(ABC\\), there are 3 distinct points on side \\(AB\\), 4 distinct points on side \\(BC\\), and 5 distinct points on side \\(AC\\). How many different quadrilaterals can be formed by selecting four of these points? I considered two cases: (1) two collinear points on one side and two points on different sides, and (2) two points on one side and two points on another side. My calculation is as follows:\n",
      "\\[\n",
      "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
      "\\]\n",
      "- Is this method and the resulting expression accurate? - Could you provide a detailed, step-by-step verification of the logic and calculations? - Could you confirm the correct usage of combinatorial formulas? - Are there alternative approaches to solving this problem worth considering?\n",
      "\n",
      "Step 4:\n",
      "#Finally Rewritten Instruction# Please meticulously verify my approach to solving the problem using combinatorial formulas: In triangle \\(ABC\\), how many quadrilaterals can be formed by selecting four points from 3 on side \\(AB\\), 4 on side \\(BC\\), and 5 on side \\(AC\\)? I considered cases (1) and (2), and calculated:\n",
      "\\[\n",
      "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
      "\\]\n",
      "Is this method and expression accurate? Could you confirm the correct usage of combinatorial formulas and consider alternative problem-solving strategies?\n",
      "```\n",
      "  2. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List#\n",
      "1. Incorporate technical terms related to combinatorics and geometry.\n",
      "2. Request for a detailed explanation of each step in the solution.\n",
      "3. Ask for a generalization of the problem to n points on each side.\n",
      "4. Suggest an alternative method to verify the solution, such as a graphical representation.\n",
      "5. Inquire about the conditions under which the solution would not hold.\n",
      "\n",
      "Step 2:\n",
      "#Plan#\n",
      "1. Add a request for the use of combinatorial identities in the explanation.\n",
      "2. Ask for a graphical representation of the quadrilaterals formed.\n",
      "3. Generalize the problem to n points on each side and ask for a formula.\n",
      "\n",
      "Step 3:\n",
      "#Rewritten Instruction#\n",
      "Please verify my approach to solving the following problem: In triangle ABC, there are 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side AC. How many different quadrilaterals can be formed by selecting four of these points? I considered two cases: (1) two collinear points on one side and two points on different sides, and (2) two points on one side and two points on another side. My calculation is as follows:\n",
      "$$\n",
      "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
      "$$\n",
      "Is this method and the resulting expression accurate? Please provide a detailed explanation using combinatorial identities, and also include a graphical representation of the quadrilaterals formed. Furthermore, could you generalize this problem to n points on each side and provide a formula for the number of quadrilaterals that can be formed?\n",
      "\n",
      "Step 4:\n",
      "#Finally Rewritten Instruction#\n",
      "Please rigorously verify my approach to solving the following problem: In triangle ABC, there are 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side AC. How many different quadrilaterals can be formed by selecting four of these points? I considered two cases: (1) two collinear points on one side and two points on different sides, and (2) two points on one side and two points on another side. My calculation is as follows:\n",
      "$$\n",
      "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
      "$$\n",
      "Is this method and the resulting expression accurate? Please provide a detailed explanation using combinatorial identities, and also include a graphical representation of the quadrilaterals formed. Additionally, could you generalize this problem to n points on each side?\n",
      "```\n",
      "  3. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List#\n",
      "1. Introduce additional constraints or conditions.\n",
      "2. Require the verification of the method's validity under certain conditions.\n",
      "3. Request a detailed explanation for each step of the method.\n",
      "4. Ask for alternative methods or approaches to solve the problem.\n",
      "5. Include a request for a proof or justification of the final answer.\n",
      "\n",
      "Step 2:\n",
      "#Plan#\n",
      "1. Add a condition that the quadrilaterals must not have any sides parallel to the triangle's sides.\n",
      "2. Request the verification of the method's validity when considering the additional condition.\n",
      "3. Ask for a detailed explanation of how the combination formula is applied in each term of the expression.\n",
      "4. Inquire about possible alternative methods to solve the problem, considering the new condition.\n",
      "5. Require a proof or justification that the final answer meets the given conditions and is mathematically sound.\n",
      "\n",
      "Step 3:\n",
      "#Rewritten Instruction#\n",
      "Please verify my approach to solving the following problem under the condition that no sides of the quadrilaterals are parallel to the triangle's sides: In triangle ABC, there are 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side AC. How many different quadrilaterals can be formed by selecting four of these points? I considered two cases: (1) two collinear points on one side and two points on different sides, and (2) two points on one side and two points on another side. My calculation is as follows:\n",
      "\n",
      "$$\n",
      "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
      "$$\n",
      "\n",
      "Is this method and the resulting expression accurate when considering the additional condition? Please provide a detailed explanation for each step of the method and suggest alternative methods if applicable. Additionally, please provide a proof or justification that the final answer meets the given conditions and is mathematically sound.\n",
      "\n",
      "Step 4:\n",
      "#Finally Rewritten Instruction#\n",
      "Please verify my approach to solving the following problem under the condition that no sides of the quadrilaterals are parallel to the triangle's sides: In triangle ABC, there are 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side AC. How many different quadrilaterals can be formed by selecting four of these points, ensuring no sides are parallel to the triangle's sides? I considered two cases: (1) two collinear points on one side and two points on different sides, and (2) two points on one side and two points on another side. My calculation is as follows:\n",
      "\n",
      "$$\n",
      "3 \\cdot 4 \\binom{5}{2} + 3 \\cdot 5 \\binom{4}{2} + 4 \\cdot 5 \\binom{3}{2} + \\binom{3}{2} \\binom{4}{2} + \\binom{3}{2} \\binom{5}{2} + \\binom{4}{2} \\binom{5}{2}\n",
      "$$\n",
      "\n",
      "Is this method and the resulting expression accurate when considering the additional condition? Please provide a detailed explanation for each step of the method and suggest alternative methods if applicable. Additionally, please provide a proof or justification that the final answer meets the given conditions and is mathematically sound.\n",
      "```\n",
      "\n",
      "Feedbacks:\n",
      "  1. ### PASSED\n",
      "  2. ### PASSED\n",
      "  3. ### PASSED\n",
      "\n",
      "Stage 2\n",
      "Evolved Instructions:\n",
      "  1. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List# Incorporate a scenario with a complex geometric figure; request evaluation of multiple calculation techniques; demand detailed reasoning for each step; seek identification of possible miscalculations.\n",
      "\n",
      "Step 2:\n",
      "#Plan# Design an instruction focusing on a scenario with a non-standard geometric shape; ask for comparison of the given solution with alternative calculation methods; require a thorough explanation of the logic behind each step; prompt for spotting and justifying potential calculation errors.\n",
      "\n",
      "Step 3:\n",
      "#Rewritten Instruction Process# Given a complex geometric shape with irregularly distributed points, how would you calculate the number of unique triangles that can be formed? Compare the provided solution to other calculation methods, explain the rationale for each step taken, and identify any potential miscalculations.\n",
      "\n",
      "Step 4:\n",
      "#Review Process# Ensure the instruction includes the complex scenario, comparison of methods, detailed rationale, and error identification. Adjust if necessary for clarity and answerability while increasing complexity.\n",
      "\n",
      "Step 5:\n",
      "#Finally Rewritten Instruction Process# Considering a complex, irregular geometric shape with unevenly distributed points, evaluate the method for calculating the number of unique triangles that can be formed. Compare this method to alternatives, thoroughly explain the logic for each step, and critically assess any potential sources of miscalculation. Is the chosen method the most efficient and accurate given the complexity of the shape and the distribution of points?\n",
      "```\n",
      "  2. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List# Incorporate a scenario with a complex geometric figure; ask for comparison of multiple calculation methods; require detailed explanation of each calculation step; identify possible errors in provided solutions.\n",
      "\n",
      "Step 2:\n",
      "#Plan# Rewrite the instruction to include a scenario with a complex geometric figure; ask for a comparison of the given solution with alternative calculation methods; require a detailed explanation of each calculation step; identify and justify possible errors in the provided solutions.\n",
      "\n",
      "Step 3:\n",
      "#Rewritten Instruction Process# Consider a scenario with a complex geometric figure composed of multiple shapes. How would you calculate the total area of this figure using the given solution? Compare this method with alternative calculation approaches, explain each step in detail, and identify any possible errors in the provided solution.\n",
      "\n",
      "Step 4:\n",
      "#Review Process# Ensure the rewritten instruction includes the scenario, comparison of methods, detailed explanation of steps, and identification of errors. Adjust if necessary to maintain clarity and answerability while increasing complexity.\n",
      "\n",
      "Step 5:\n",
      "#Finally Rewritten Instruction Process# In a scenario involving a complex geometric figure composed of multiple shapes, calculate the total area using the given solution. Compare this method with alternative approaches, explain each step in detail, and identify any possible errors in the provided solution. Is the given method efficient and accurate for the scenario, considering alternative calculation approaches?\n",
      "```\n",
      "  3. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List# Incorporate hypothetical scenarios involving different triangle configurations; ask for evaluation of alternative calculation methods; require explanation of each step in the solution, including assumptions and reasoning; identify potential errors in the given approach.\n",
      "\n",
      "Step 2:\n",
      "#Plan# Rewrite the instruction to consider a hypothetical scenario where the triangle's sides have varying numbers of points; ask for an evaluation of the given solution against alternative methods; require a detailed explanation of the reasoning behind each step, including any assumptions made; identify and justify any potential errors in the calculation.\n",
      "\n",
      "Step 3:\n",
      "#Rewritten Instruction Process# In a scenario where a triangle ABC has 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side CA, calculate the number of different triangles that can be formed by selecting three points. Evaluate the provided solution against alternative methods, explain the rationale behind each step, and identify any potential errors.\n",
      "\n",
      "Step 4:\n",
      "#Review Process# Ensure the rewritten instruction includes the scenario, evaluation of methods, detailed explanation, and error identification. Adjust as necessary to maintain clarity and answerability while increasing complexity.\n",
      "\n",
      "Step 5:\n",
      "#Finally Rewritten Instruction Process# In a scenario where a triangle ABC has 3 distinct points on side AB, 4 distinct points on side BC, and 5 distinct points on side CA, calculate the number of different triangles that can be formed by selecting three points. Evaluate the provided solution against alternative methods, explain the rationale behind each step, and identify any potential errors. Consider the accuracy of the method and the validity of the assumptions made in this complex scenario.\n",
      "```\n",
      "\n",
      "Feedbacks:\n",
      "  1. ### PASSED\n",
      "  2. ### PASSED\n",
      "  3. ### FAILED - Reason: The complexity did not increase from stage 0 to stage 1. In stage 0, the instruction involves calculating the number of different quadrilaterals that can be formed, while in stage 1, it is reduced to calculating the number of different triangles, which is less complex.\n",
      "\n",
      "Stage 3\n",
      "Evolved Instructions:\n",
      "  1. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List#\n",
      "1.1. Introduce multi-level hypothetical scenarios.\n",
      "1.2. Evaluate multiple solutions and their pros and cons.\n",
      "1.3. Explain steps and reasoning, identifying potential errors.\n",
      "1.4. Incorporate meta-cognitive questions.\n",
      "1.5. Predict challenges and propose solutions.\n",
      "\n",
      "Step 2:\n",
      "#Plan#\n",
      "2.1. Consider alternative calculation methods.\n",
      "2.2. Explain reasoning and identify errors.\n",
      "2.3. Reflect on decision-making process.\n",
      "2.4. Predict challenges in calculation.\n",
      "2.5. Ensure rewritten instruction is more complex.\n",
      "\n",
      "Step 3:\n",
      "#Elaborate on Methods#\n",
      "3.1. Design scenarios that challenge AI's outcome analysis.\n",
      "3.2. Provide detailed analysis of solution advantages and disadvantages.\n",
      "3.3. Explain reasoning and implications of potential errors.\n",
      "3.4. Encourage reflection on thought process and decision-making.\n",
      "3.5. Propose solutions to identified challenges.\n",
      "\n",
      "Step 4:\n",
      "#Execute the Plan#\n",
      "Rewrite instruction with multi-level scenarios, evaluate methods, explain steps, reflect, and predict challenges.\n",
      "\n",
      "Step 5:\n",
      "#Review the Rewritten Instruction#\n",
      "Ensure the rewritten instruction is significantly more complex, clear, and answerable.\n",
      "\n",
      "Step 6:\n",
      "#Finally Rewritten Instruction#\n",
      "In a multi-level hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, with points that can move along the sides, devise and evaluate multiple methods to calculate the number of different quadrilaterals that can be formed by selecting four points. Explain the rationale behind each step, identify potential errors, and reflect on the decision-making process. Predict possible challenges in the calculation due to moving points and propose robust solutions to overcome them, considering the implications on the solution's robustness.\n",
      "```\n",
      "  2. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List#\n",
      "1.1. Introducing multi-level hypothetical scenarios.\n",
      "1.2. Evaluating multiple solutions and their pros and cons.\n",
      "1.3. Explaining steps and identifying potential errors.\n",
      "1.4. Incorporating meta-cognitive reflection.\n",
      "1.5. Predicting challenges and proposing solutions.\n",
      "\n",
      "Step 2:\n",
      "#Plan#\n",
      "2.1. Challenge the AI to consider moving points and their implications.\n",
      "2.2. Ask for the evaluation of the provided solution and alternatives.\n",
      "2.3. Require detailed explanations and error identification.\n",
      "2.4. Encourage reflection on the decision-making process.\n",
      "2.5. Request prediction of challenges and solutions.\n",
      "\n",
      "Step 3:\n",
      "#Elaborate on Methods#\n",
      "3.1. Design scenarios that test understanding of moving points.\n",
      "3.2. Conduct a detailed analysis of solution effectiveness.\n",
      "3.3. Explain each decision and identify possible calculation errors.\n",
      "3.4. Reflect on the reasoning behind each choice.\n",
      "3.5. Propose solutions for potential calculation challenges.\n",
      "\n",
      "Step 4:\n",
      "#Execute the Plan#\n",
      "Rewrite the instruction to include multi-level scenarios, evaluate solutions, explain steps, reflect on decision-making, and predict challenges.\n",
      "\n",
      "Step 5:\n",
      "#Review the Rewritten Instruction#\n",
      "Ensure the rewritten instruction is complex, clear, and answerable, with all elements from the plan incorporated.\n",
      "\n",
      "Step 6:\n",
      "#Finally Rewritten Instruction#\n",
      "In a multi-level scenario with a moving-point triangle XYZ, calculate the number of possible quadrilaterals formed by selecting four points, considering robustness against errors. Evaluate the provided solution and alternatives, explaining each step and identifying potential errors. Reflect on your decision-making and predict challenges, proposing solutions. Consider the implications of point movement on the solution's robustness.\n",
      "```\n",
      "  3. ```Optimized Instruction\n",
      "Step 1:\n",
      "#Methods List#\n",
      "1.1. Introduce multi-level hypothetical scenarios involving moving points and side lengths.\n",
      "1.2. Evaluate multiple solutions for calculating quadrilaterals, considering their robustness and efficiency.\n",
      "1.3. Explain each step in detail, including the reasoning behind decisions and potential errors.\n",
      "1.4. Reflect on the decision-making process and thought process.\n",
      "1.5. Predict challenges such as point movement and propose error-checking mechanisms.\n",
      "\n",
      "Step 2:\n",
      "#Plan#\n",
      "2.1. Create a scenario where points can move dynamically on the triangle's sides.\n",
      "2.2. Evaluate three methods for calculating quadrilaterals, discussing their advantages and disadvantages.\n",
      "2.3. Explain each step, identifying potential errors and their implications.\n",
      "2.4. Incorporate meta-cognitive questions about the decision-making process.\n",
      "2.5. Predict possible challenges and propose solutions to ensure accuracy.\n",
      "\n",
      "Step 3:\n",
      "#Elaborate on Methods#\n",
      "3.1. Design a scenario that challenges the AI to consider the implications of point movement on quadrilateral formation.\n",
      "3.2. Evaluate methods in terms of computational efficiency and robustness against errors.\n",
      "3.3. Explain each decision, identifying potential errors and their impact on the final result.\n",
      "3.4. Ask reflective questions about the decision-making process and the thought process behind each step.\n",
      "3.5. Predict challenges related to point movement and propose mechanisms to check for errors and ensure accuracy.\n",
      "\n",
      "Step 4:\n",
      "#Execute the Plan#\n",
      "Rewrite the instruction to include a multi-level scenario with moving points. Evaluate three methods for calculating quadrilaterals, discussing their efficiency and robustness. Explain each step, including the reasoning and potential errors. Incorporate reflective questions about the decision process. Predict challenges and propose solutions to ensure accuracy.\n",
      "\n",
      "Step 5:\n",
      "#Review the Rewritten Instruction#\n",
      "Ensure the rewritten instruction is significantly more complex, includes all elements of the plan, and is clear and answerable. Adjust to maintain clarity and complexity.\n",
      "\n",
      "Step 6:\n",
      "#Finally Rewritten Instruction#\n",
      "In a multi-level scenario where points on the sides of triangle XYZ can dynamically move, calculate the number of different quadrilaterals that can be formed. Evaluate three methods for calculation, discussing their efficiency and robustness. Explain each step, identifying potential errors and their implications. Reflect on the decision-making process and thought process. Predict challenges related to point movement and propose error-checking mechanisms to ensure the accuracy of your solution.\n",
      "```\n",
      "\n",
      "Feedbacks:\n",
      "  1. ### PASSED\n",
      "  2. ### PASSED\n",
      "  3. ### PASSED\n",
      "\n",
      "Final Instruction: In a multi-level hypothetical scenario where a triangle XYZ has 4 distinct points on side XY, 5 distinct points on side YZ, and 6 distinct points on side ZX, with points that can move along the sides, how would you calculate the number of different quadrilaterals that can be formed by selecting four points? Evaluate the provided solution and alternative methods, including their advantages and disadvantages, explain the rationale behind each step, and identify any potential errors in the calculation. Reflect on the decision-making process and predict possible challenges in the calculation, proposing solutions to overcome them. Consider the implications of moving points and the robustness of your solution against potential errors, while also contemplating the adaptability of your strategy based on previous errors in similar problems.\n"
     ]
    }
   ],
   "source": [
    "analyze_single_sample(data[600])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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